The superior accuracy achieved by our model comes at the cost of slightly higher complexity. Our network counts 16M weights versus the 300K of NCNN variants [1]}, leading to higher runtime on both 3090 and Jetson TX2 GPUs – respectively 0.02 and 1.02 seconds by our model versus 0.01 and 0.31 required by NCNN variants [1]}. However, our model achieves better results and does not require any ground-truth depth label for training. For completeness, we also report the runtime required by Surface [3]}. Although implemented on CPU, thus not directly comparable with our method and NCNN, [3]} takes, respectively, 0.43 and 2.63 seconds on the same desktop PC equipped with a 3090 GPU – and an i9-10900X – and the Jetson TX2 CPU.
